%0 Journal Article
%A Mokhtari Dehkordi, Ramin
%A Akhoondzadeh, Mahdi
%T Combining Neural Network and Wavelet Transform to Predict Drought in Iran Using MODIS and TRMM Satellite Data
%J Journal of Geospatial Information Technology
%V 7
%N 4
%U http://jgit.kntu.ac.ir/article-1-769-en.html
%R 10.29252/jgit.7.4.175
%D 2020
%K Drought, Wavelet Artifical Neural Networks, Time Series, NDVI.,
%X The drought can be described as a natural disaster in each region. In this study, one of the important factors in drought, vegetation, has been considered. For this purpose, monthly vegetation cover images and snow cover data of MODIS and TRMM satellite precipitation product from 2009 to 2018 were used for the study area of Iran. After initial preprocessing, we have used artificial neural network method and hybrid neural network and wavelet transform method to predict the normalized difference vegetation index (NDVI). After training the two algorithms using the time series of (NDVI) index as well as the time series of snow cover and precipitation from 2009 to 2017, the (NDVI) index is predicted for twelve months from 2018, which is finally estimated with real values. The results and prediction accuracy for these two algorithms are different and in general the combined neural network and wavelet transform method has higher accuracy compare to the neural network method so that the twelve average of 2018 is equal to the root mean square error of 0.055 and coefficient of determination was 0.804. The results also show that in both methods the accuracy of the index in the early months of 2018 is better than the end months. Therefore, this method can be used to predict this index, as one of the drought parameters.
%> http://jgit.kntu.ac.ir/article-1-769-en.pdf
%P 175-191
%& 175
%!
%9 Research
%L A-11-371-9
%+ University of Tehran
%G eng
%@ 2008-9635
%[ 2020